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arXiv · 2505.20255

AniCrafter: Customizing Realistic Human-Centric Animation via Avatar-Background Conditioning in Video Diffusion Models

Muyao Niu·Mingdeng Cao·Yifan Zhan·Qingtian Zhu·Weihang Ran·Yanhong Zeng·Xiao Sun·Zhihang Zhong·Yinqiang Zheng
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Abstract

Recent advances in video diffusion models have substantially enhanced character animation techniques. However, existing methods primarily depend on structural conditions, such as DWPose or SMPL-X, to animate character images, which limits their effectiveness in open-domain scenarios involving dynamic backgrounds or complex character-scene interactions. This study presents AniCrafter, a diffusion-based human-centric animation model designed to seamlessly integrate and animate a given character within open-domain dynamic backgrounds while adhering to specified human motion sequences. Built upon advanced Image-to-Video (I2V) diffusion architectures, the model introduces an innovative "avatar-background" conditioning mechanism that reformulates open-domain human-centric animation as a restoration problem, thereby achieving versatile, occlusion-aware animation results. Experimental evaluations demonstrate that the proposed approach outperforms current state-of-the-art methods and exhibits an exceptional capability in handling challenging scenarios. Codes and model are available at: https://github.com/MyNiuuu/AniCrafter

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Muyao Niu, Mingdeng Cao, Yifan Zhan, Qingtian Zhu, Weihang Ran, Yanhong Zeng, Xiao Sun, Zhihang Zhong, Yinqiang Zheng. 2025-05-26. AniCrafter: Customizing Realistic Human-Centric Animation via Avatar-Background Conditioning in Video Diffusion Models. https://arxiv.org/abs/2505.20255

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